Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication
By Woongsup Lee, Minhoe Kim, Dong Ho Cho. 2018
65 citations recorded by the source corpus.
A transmit power control strategy using a deep neural network (DNN) is proposed for underlay device-to-device (D2D) communication where D2D user equipment (DUE) shares radio resources with cellular user equipment (CUE). In this scheme, a transmit power control strategy for DUE is found with the aid of a newly proposed DNN structure. Both the spectral efficiency (SE) of the DUE and the amount of interference at the CUE are taken into account, such that the SE of the DUE can be improved while alleviating any deterioration in the cellular transmission. Using simulations, we show that the proposed scheme can achieve a high SE of the DUE while properly regulating the interference caused to the CUE, with a low computation time.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
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Citations in the corpus
- Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “To resolve this issue, a penalizing strategy is applied to transform the original training task to an unconstrained training one by augmenting a penalty term associated with the IT constraint into the network utility function [10], [11].”
- Constrained Deep Learning for Wireless Resource Management (2019, methodology): “As a result, unlike the conventional penalizing strategies in [5] and [6] which require additional hyper parameter identification process, the feasibility of the proposed training algorithm is guaranteed by jointly optimizing the”